Acquisition and analysis techniques for improved outcomes in optical coherence tomography angiography

ABSTRACT

Methods for improved acquisition and processing of optical coherence tomography (OCT) angiography data are presented. One embodiment involves improving the acquisition of the data by evaluating the quality of different portions of the data to identify sections having non-uniform acquisition parameters or non-uniformities due to opacities in the eye such as floaters. The identified sections can then be brought to the attention of the user or automatically reacquired. In another embodiment, segmentation of layers in the retina includes both structural and flow information derived from motion contrast processing. In a further embodiment, the health of the eye is evaluating by comparing a metric reflecting the density of vessels at a particular location in the eye determined by OCT angiography to a database of values calculated on normal eyes.

PRIORITY

The present application claims priority to U.S. Provisional ApplicationSer. No. 62/112,585 filed Feb. 5, 2015, the contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The present application relates to ophthalmic imaging, and in particularto acquisition and analysis methods for vasculature image data acquiredthrough interferometric imaging techniques such as optical coherencetomography.

BACKGROUND

Optical coherence tomography (OCT) is widely recognized as a powerfulophthalmic imaging technique. Optical coherence tomography (OCT)angiography techniques, such as optical microangiography (OMAG), specklevariance, phase variance, etc., use OCT systems to achieve the imagingof flow and motion within a tissue, including imaging of functionalvascular networks within microcirculatory tissue beds in vivo, withoutthe use of exogenous contrast agents. A majority of ocular diseases maylead to abnormality in microvasculature beds in the eye, includingdiabetic retinopathy (DR), age-related macular degeneration (AMD),glaucoma, retinal vein occlusion etc. OCT angiography can be anon-invasive way to be able to diagnose and monitor such vascularabnormalities. However, it is very critical to be able to obtain highquality images of vasculature and perform reliable analysis fordiagnosis and monitoring of diseases.

There are several limitations in the state-of-the-art OCT angiographytechnology that makes it difficult to consistently acquire high qualityclinical data sets. One of the major challenges of OCT angiography forlarger field of view (FOV) scans is the variability of signal levelsfrom different regions of the eye. This variability can be caused bymany factors including subject motion and non-optimized refractivecorrection in different regions of eye. On the analysis side, there areseveral challenges as well, such as the capability to segment tissuelayers with greater accuracy. Sometimes, spatially localized opacity,such as the one caused by a floater in the vitreous or by the opacity ofthe lens of the eye, may project dark pockets in the vasculature beds,thereby causing uncertainty in the diagnosis whether it is the loss ofvasculature (ischemia) or shadow by a floater. In addition it will alsobe desirable to be able to develop analyses that could aid in detectingearly vasculature based symptoms due to a disease like DR.

SUMMARY

This application describes various acquisition and analysis techniquesto improve the output of OCT angiography. These are described asfollows:

Increased Uniformity of OCT Angiography Data Collected Over Large FOV inthe Eye

During long acquisitions in OCT angiography over large FOVs, either insingle cube or montage type acquisitions, the likelihood of non-uniformacquisition increases because of many reasons. The patients get tiredand the signal may vary in different regions of the eye, due to factorsincluding Z-motion, pupil shifts, non-optimized focus, irregular tearfilm due to drying during the setup and acquisition of images, shadowingetc. Methods are described to ensure increased uniformity in dataquality over large FOVs. In one approach, regions of low signal data areidentified after the scan is finished and the data is reacquired fromthe identified region.

Identify Shadow Artifacts for Improved Assessment of CapillaryNon-Perfusion in OCT Angiography

One of the issues when looking into OCT angiography images to assessnon-perfusion is when there is a shadowing effect from some opacity inthe path of the light, such as a floater in the vitreous etc. We canidentify the enface region that is impacted by the shadow artifact andlet the user know that in this region, the vasculature mapping of thetissue is not reliable. Hence, false positives caused by floaters orother media opacities can be minimized.

Improved Segmentation Capabilities by Using Motion-Contrast Data forSegmentation Purposes

Typical segmentation algorithms are based on structural or intensity OCTdata. However, for some anatomical layers such as choriocapillaris etc.,structural OCT data may not provide sufficient information for tissuelayer differentiation, or may not provide information with as gooddefinition in the axial direction. For tissue layers that containvasculature beds, it will be useful to analyze motion-contrast data tobe able to segment those layers. Information about the segmented layersfrom intensity OCT data may be used as an input to further aid thesegmentation based on motion-contrast data. In the preferred embodiment,RPE information from the intensity OCT data can be used to localize theregion of interest for segmentation of choriocapillaris data based onmotion-contrast data.

Eye Health Indication

It is possible to compare the density of the microvasculature around thefovea to a database of normal eyes to determine if there may be damagerelated to diabetes or other diseases. In particular, this couldpotentially be done without a full cube scan of the macula. A circlescan at a particular distance from the fovea may be sufficient todistinguish the status of the eye, which could be done with a slower OCTsystem. Because only a cluster of circle scans of a single radius isacquired, it may be possible to dispense with tracking. A slow systemwith no tracking could be relatively inexpensive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a generalized OCT system that can be used to collectOCT angiography data.

FIG. 2 shows a typical en face vasculature OCT angiography image of theretina.

FIG. 3 shows a wide-field OCT angiography en face vasculature image of aretina having uniform imaging parameters over the entire field of view.

FIG. 4 shows a wide-field OCT angiography en face vasculature image of aretina where sections show non-uniform acquisition parameters relativeto the rest of the data.

FIG. 5 shows a structural B-scan image of the retina with the retinalpigment epithelium (RPE) layer indicated.

FIG. 6 shows the segmentation of the RPE on a zoomed in portion of thestructural B-scan shown in FIG. 5.

FIG. 7 shows a motion contrast B-scan of the same retina shown in FIGS.5-6 and the segmentations from the structural and motion contrast data.

FIG. 8 is a zoomed in version of the box of data from FIG. 7.

FIG. 9a illustrates the vasculature density of the superficial retinallayer (SRL) as a function of distance from the boundary of the fovealavascular zone for a plurality of eyes and two normative limits.

FIG. 9b illustrates the vasculature density of the deeper retinal layer(DRL) as a function of distance from the boundary of the fovealavascular zone for a plurality of eyes and two normative limits.

DETAILED DESCRIPTION

Illustrative embodiments are now described. Other embodiments may beused in addition or instead. Details that may be apparent or unnecessarymay be omitted to save space or for a more effective presentation. Someembodiments may be practiced with additional components or steps and/orwithout all of the components or steps that are described.

The components, steps, features, objects, benefits, and advantages thathave been discussed are merely illustrative. None of them, nor thediscussions relating to them, are intended to limit the scope ofprotection in any way. Numerous other embodiments are also contemplated.These include embodiments that have fewer, additional, and/or differentcomponents, steps, features, objects, benefits, and/or advantages. Thesealso include embodiments in which the components and/or steps arearranged and/or ordered differently.

The OCT system may comprise any type of OCT system. Examples of the OCTsystems may include Time-domain OCT (TD-OCT) and Fourier-domain, orFrequency-domain, OCT (FD-OCT). Examples of the FD-OCT may includespectral-domain OCT (SD-OCT), swept Source OCT (SS-OCT), and opticalfrequency domain Imaging (OFDI). The OCT technique can involve pointscanning, line scanning, partial field scanning, or full fieldillumination of light on a sample.

A diagram of a generalized OCT system is shown in FIG. 1. Light fromsource 101 is routed, typically by optical fiber 105, to illuminate thesample 110, a typical sample being tissues in the human eye. The source101 can be either a broadband light source with short temporal coherencelength in the case of SD-OCT or a wavelength tunable laser source in thecase of SS-OCT. The light is scanned, typically with a scanner 107between the output of the fiber and the sample, so that the beam oflight (dashed line 108) is scanned laterally (in x and y) over the areaor volume to be imaged. Light scattered from the sample is collected,typically into the same fiber 105 used to route the light for sampleillumination. Reference light derived from the same source 101 travels aseparate path, in this case involving fiber 103 and retro-reflector 104with an adjustable optical delay. Those skilled in the art recognizethat a transmissive reference path can also be used and that theadjustable delay could be placed in the sample or reference arm of theinterferometer. Collected sample light is combined with reference light,typically in a fiber coupler 102, to form light interference in adetector 120. Although a single fiber port is shown going to thedetector, those skilled in the art recognize that various designs ofinterferometers can be used for balanced or unbalanced detection of theinterference signal. The output from the detector is supplied to aprocessor 121. The results can be stored in the processor 121 ordisplayed on display 122. The processing and storing functions may belocalized within the OCT instrument or functions may be performed on anexternal processing unit to which the collected data is transferred.This unit could be dedicated to data processing or perform other taskswhich are quite general and not dedicated to the OCT device. Theprocessor may contain for example a field-programmable gate array(FPGA), a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a graphics processing unit (GPU), a system onchip (SoC) or a combination thereof, that performs some, or the entireangiography data processing steps, prior to passing on to the hostprocessor or in a parallelized fashion.

The sample and reference arms in the interferometer could consist ofbulk-optics, fiber-optics or hybrid bulk-optic systems and could havedifferent architectures such as Michelson, Mach-Zehnder or common-pathbased designs as would be known by those skilled in the art. Light beamas used herein should be interpreted as any carefully directed lightpath. In time-domain systems, the reference arm needs to have a tunableoptical delay to generate interference. Balanced detection systems aretypically used in TD-OCT and SS-OCT systems, while spectrometers areused at the detection port for SD-OCT systems. The invention describedherein could be applied to any type of OCT system capable of generatingdata for OCT angiography analysis. The techniques described herein couldbe applicable to any body parts, for example eye (both anterior andposterior chambers), skin, brain, muscle, cochlear, and internal organsif integrated with endoscope or catheter probe.

In Fourier Domain optical coherence tomography (FD-OCT), eachmeasurement is the real-valued spectral interferogram (S_(j)(k)). Thereal-valued spectral data typically goes through several postprocessingsteps including background subtraction, dispersion correction, etc. TheFourier transform of the processed interferogram, results in a complexvalued OCT signal output A_(j)(z)=|A_(j)|e^(iφ). The absolute value ofthis complex OCT signal, |A_(j)|, reveals the profile of scatteringintensities at different path lengths, and therefore scattering as afunction of depth (z-direction) in the sample. Similarly, the phase,φ_(j) can also be extracted from the complex valued OCT signal. Theprofile of scattering as a function of depth is called an axial scan(A-scan). A set of A-scans measured at neighboring locations in thesample produces a cross-sectional image (tomogram or B-scan) of thesample. A collection of B-scans collected at different transverselocations on the sample makes up a data volume or cube. For a particularvolume of data, the term fast axis refers to the scan direction along asingle B-scan whereas slow axis refers to the axis along which multipleB-scans are collected. We use the term “cluster scan” herein to refer toa single unit or block of data generated by repeated acquisitions at thesame location for the purposes of analyzing motion contrast. A clusterscan can consist of multiple A-scans or B-scans collected over time atapproximately the same location(s) on the sample. A variety of ways tocreate B-scans are known to those skilled in the art including but notlimited to along the horizontal or x-direction, along the vertical ory-direction, along the diagonal of x and y, or in a circular or spiralpattern. The majority of the examples discussed herein refer to B-scansin the x-z dimensions but the invention would apply equally to any crosssectional image.

In OCT Angiography (a.k.a. Functional OCT, optical microangiography,motion contrast OCT), changes between the OCT data collected at the samelocation at different times (cluster scans) are used to analyze motionor flow in the sample using any one of a multitude of motion contrastalgorithms (see for example U.S. Patent Publication Nos. 2005/0171438,2012/0307014, 2010/0027857, 2012/0277579, U.S. Pat. No. 6,549,801,Mariampillai et al., “Speckle variance detection of microvasculatureusing swept-source optical coherence tomography”, Optics Letters 33(13), 1530-1533, 2008, Enfield et al., “In vivo imaging of themicrocirculation of the volar forearm using correlation mapping opticalcoherence tomography” (cmOCT), Biomed. Opt. Express 2 (5), 1184-1193,2011, Nam et al. “Complex differential variance algorithm for opticalcoherence tomography angiography” Biomedical Optics Express 5 (11)3822-3832 2014, and Jia et al. “Split-spectrum amplitude decorrelationangiography with optical coherence tomography” Optics Express 20 (4)4710-4725 (2012), the contents of which are hereby incorporated byreference). Motion contrast algorithms can be applied to the intensityinformation derived from the OCT image data (intensity-based algorithm),the phase information from the OCT image data (phase-based algorithm),or the complex OCT image data (complex-based algorithm).

An en face vasculature image is an image displaying the motion contrastsignal in which the data dimension corresponding to depth is displayedas a single representative value, typically by summing or integrating anisolated portion of the data (see for example U.S. Pat. No. 7,301,644hereby incorporated by reference). An example of an en face vasculatureimage is shown in FIG. 2. For this image, three volumes (cubes) of datawere collected with some overlapping area in the retina. The enfaceimages obtained from the three volumes were montaged, or combined, tocreate a larger field of view enface image. Each B-scan in a given datavolume consists of 300 A-scans, each cluster scan consists of fourB-scans, and there are a total of eighty different cluster scans. Hence,the number of A-scans in a given unit data volume are 300×80×4. Afterprocessing the data to highlight motion contrast using any one of theknown motion contrast techniques, a range of 50-60 pixels correspondingto 100-120 microns of tissue depth from the surface of internal limitingmembrane (ILM) in retina, are summed to generate an en face image of thevasculature. Each B-scan takes approximately 12 ms to acquire (includingfly-back time) so the time between B-scans is approximately 12 ms whichis on the order of interest for retinal vasculature dynamics.

There are several limitations in the state-of-the-art OCT angiographytechnology as well as in commercial OCT systems that makes it difficultto consistently acquire high quality clinical data sets and derivemeaningful clinical analysis. This application describes variousacquisition and analysis techniques to improve the results of OCTangiography imaging.

Increased uniformity of OCT angiography data collected over large FOV inthe eye During long acquisitions in OCT Angiography over a large fieldof view (FOV), either in single cube or montage type acquisitions, thelikelihood of non-uniform acquisition conditions may increase. In U.S.Pat. No. 8,857,988, hereby incorporated by reference, we described theuse of tracking to aid in the collection of OCT angiography data in bothsingle and multiple volume acquisitions to avoid contamination frommotion artifacts that become more likely the longer the scan acquisitiontakes. Here we propose several ideas to improve the acquisition of OCTangiography data collection over large fields of view where variationsin the curvature of the eye or the focus can lead to non-uniformities inthe collected data.

FIG. 3 shows a wide-field enface OCT angiography vasculature image(FOV˜7 mm×6 mm) obtained by montaging of 9 data cubes in a 3×3 gridmanner. It can be seen from the montage enface image that the signalcharacteristics are similar across the different cubes, and one couldconclude that all the 9 data cubes were acquired under uniformacquisition conditions such that there were no significant Z-motionshifts, pupil shifts or non-optimized focus, etc. Hence it is possibleto obtain reliable clinical information about the vasculature from thiswide field image. In contrast, in the montaged image shown in FIG. 4,the middle and right cubes (401) in the bottom row had differentacquisition conditions leading to significantly different data qualitycompared to the adjacent regions. Such an outcome will not be acceptableas it is difficult to make useful and accurate clinical diagnosis basedon this image.

In one embodiment of this application, an OCT angiography data scancomprising at least one cluster scan is completed, regions ofsub-optimal data (e.g. low signal data) are identified after the scan isfinished, and the data is either automatically reacquired from theidentified region or the operator is prompted to reacquire it, e.g. witha message displayed on the screen of the instrument. The identificationof sub-optimal data can be carried out on the structural (intensity) OCTdata before the motion contrast algorithm is applied to the dataset oron the processed motion contrast data. The analysis can be carried outon a single volume or cube of data or on data collected as multipleoverlapping cubes. In the case of multiple overlapping cubes, the finalimage would result from montaging the motion contrast informationresulting from processing the multiple cubes together.

To enable identification of regions of sub-optimal data regions, aquality metric can be used. In one embodiment, a quality metric is basedon a comparison of the vasculature density or the signal strength at theoverlap area or at the edges of two cubes. A predetermined threshold forthe quality metric such as described in U.S. Patent Publication No.2015/0062590 hereby incorporated by reference could also be used to flagdifferences in acquisition parameters. Using this approach one couldprovide a montage 2D preview to the user after complete acquisition ofthe multiple image cubes, and allow or prompt the user to re-scan badcubes. Alternatively and preferably, the system would automaticallyre-collect the portions of the data identified as sub-optimal, thuscreating the most efficient work flow. One could also have the systemautomatically re-acquire only the portion of a scan that had poor signalin order to limit the total acquisition time required to collect gooddata.

In other approaches, one could improve uniformity of datacharacteristics by ensuring that the OCT beam is well-focused indifferent regions of the eye. In typical OCT systems, a singlerefractive correction is used for a given fixation. This refractioncorrection typically ensures that the retina near the fovea is broughtinto best focus for the specific optics of the eye being imaged. Becausethe eye is curved, when attempting to image parts of the eye away fromthe fovea, the distance from the pupil to the retina may change. Theuser will often make manual adjustments to the refractive correction ofthe system to obtain a better image while collecting data. Thecommercial OCT system Cirrus HD-OCT (ZEISS) offers an AutoFocuscapability which can be applied on peripheral scans as well asfovea-centered scans, but this feature takes 1-2 seconds to use. Suchoptimizations may be critical to ensure uniformity in OCT angiography,especially when collecting multiple images over different parts of theeye to cover a wide-field of view. It is desirable to automate theoptimizations to avoid the need for manual adjustment and to avoidtaking 1-2 seconds per scan type to perform the optimization. In an OCTB-scan, the retina often appears curved. This is due to a mismatchbetween the arc drawn by the OCT beam and the curvature of the portionof the eye being imaged (see for example Lujan et at. “Revealing Henle'sFiber layer using spectral domain optical coherence tomography” IOVS 52(3) 1486-1492, 2011). Because the observed curvature in the scan isrelated to the shape of the eye, and the shape of the eye affects therefractive correction required to bring the retina into focus, it may bepossible to pre-determine the amount of adjustment to the refractiveerror for a neighboring scan based on the curvature of the eye observedat the edge of one of the scans already acquired. This would be fasterand easier than manually adjusting with each acquisition or usingAutoFocus. In one embodiment, refractive corrections are individuallyoptimized automatically for different cubes within a montage scan byacquiring at least two OCT angiography volumes over partiallyoverlapping locations of the eye, where for each volume the systemidentifies the curvature of the scan at the edge to be overlapped withthe subsequent scan and adjusts the refractive correction (e.g. movingthe position of the ocular lens) established at the start of scanning toaccount for the observed curvature.

Another optimization that is frequently required when acquiring multipleimages over the retina is adjustment of the position of the pupil. Theposition of the pupil may affect the amount of light that is able toreach the portion of the retina being scanned. When part of the beam iscut off by the patient pupil this is called vignetting. The position ofthe pupil also affects the apparent tilt of the retina in the OCT B-scanas described above—the OCT beam arc is scanned through a point on thepupil, and the curve drawn out will depend on the line between the pupilentry position and the back of the eye. It is difficult to maintain asteady relationship between these two over long periods of time, and inpractice the instrument operator often has to adjust the pupil entryposition between scans. Automatically tracking the relationship betweenthe beam and the pupil would ensure the beam remains within the pupil,which prevents vignetting. Tracking the pupil can also be used to ensurethat the retina is as perpendicular to the incident beam as can beachieved. Avoiding tilt of the retinal relative to the image should helpwith stitching together multiple images. To deal with these issues, inanother embodiment of the present application, pupil tracking may beused to ensure uniform acquisition (see U.S. Patent Publication No.20120274897 hereby incorporated by reference). Pupil tracking can beused to select an optimized pupil entry location and maintain it duringscanning. Pupil tracking would effectively allow the system to flattenthe retina as it scans over a wide field of view. Because the optimalpupil position will vary with the patient's fixation, pupil positionshould be adjusted (automatically or manually) as appropriate for eachfixation position.

Identify Shadow Artifacts for Improved Assessment of CapillaryNon-Perfusion in OCT Angiography

Shadowing effects from opacities in the path of light (e.g. floaters inthe vitreous or cataracts), can complicate the interpretation of OCTangiography images in assessing non-perfusion. In one embodiment ofimproved OCT angiography data collection, the enface region(s) that isimpacted by the shadow artifact is identified and the user is informedthat in this region, the vasculature mapping of the tissue is notreliable. Hence, false positives caused by floaters can be minimized.

Several methods can be used to identify the shadow regions such asgenerating enface maps of structural data, or displaying a map of thesignal strength or segmentability within the OCT data. In yet anotherapproach, enface maps can be generated by using linear scale OCT data.It must be noted that typical enface images in OCT are generated byusing log scale OCT data. Linear scale OCT data may enhance the abilityto detect shadows in the structural OCT data. Shadow regions could beidentified in the image or could be displayed through the use of twocolor channel images, one for structure and one for function. The usermay be able to qualitatively examine a structural en face image ineither linear or log scale, as well as structural B-scans, to identifyareas affected by floaters. In one embodiment, post-processing softwarewould evaluate the structural and angiographic en face images andhighlight areas of high or low confidence. In another embodiment, theareas of low confidence would be masked. Another option would be toalert the user to the need to re-acquire data when it was impacted by afloater, or to automatically have the software re-acquire.

In addition, adjunct imaging modalities such as line scanningophthalmoscopy or some other form of fundus imaging, taken nearsimultaneously with the OCT images could be used to visualize and/orlocalize shadows due to floaters. The information about the location ofthe opacities could be displayed on the motion contrast image to conveylow confidence in the data in those areas.

Improved Segmentation Capabilities by Using Motion-Contrast Data forSegmentation Purposes

Segmentation algorithms are typically performed on structural orintensity OCT data (see for example ‘A Review of Algorithms forSegmentation of Retinal Image Data Using Optical Coherence Tomography,’Chap. 2, in Image Segmentation, Ed. P.-G. Ho, Pub: InTech, 15-54 thecontents of which are hereby incorporated by reference). However, forsome anatomical layers such as the choriocapillaris, additional datauseful to segmentation algorithms may be available in the motioncontrast data. An OCT cross sectional intensity image (B-scan) is shownin FIG. 5. This image consists of 300 A-scans. Based on microstructureinformation of the OCT intensity image, the center of the retinalpigment epithelium (RPE) layer (501) is typically segmented as the dashline 601 shown in FIG. 6. Although it looks like it accurately locatesthe position of RPE, it is not good enough to segment out thechoriocapillaris, the layer below the RPE. It also does not define thewidth of the of the RPE layer. FIG. 7 is a motion contrast imagegenerated by applying any one of a number of OCT angiography algorithmsto a plurality of OCT intensity images collected over the same region asFIG. 6. The motion contrast B-scan in FIG. 7 highlights the flow in thetissue.

The outputs of OCT angiography typically include a volume of structuraldata that is created by averaging B-scans from common locations as wellas a volume of motion contrast or flow data that is created by comparingB-scans from the same set of locations on the sample separated in time.These two volumes are inherently co-registered and contain differentinformation about the imaged volume of tissue. One example of additionalinformation that is available in the flow image includes the fact thatthe choroid has a substantially different flow signature than theretina. This can be seen in FIG. 7, which shows the retinal tissue to bedark in the flow image, while in typical structural images such as seenin FIG. 6 the retina is bright. There is an apparent sharp break in theflow image where the retina ends and the choriocapillaris begins. Thisdark to bright transition could be a potentially higher resolutionmethod for isolating the lower boundary of the RPE and upper boundary ofthe choriocapillaris than any segmentation of the structural informationfrom the retina. In graph-based segmentation methods, a cost function isbuilt up based on information such as gradients and intensity valueswithin the structural image. A cost function that includes informationfrom both the structural image and the flow image would contain moreinformation than the typical segmentation cost function based only onthe structural image and therefore serve as an improved segmentationapproach. Two other examples where information is contained in the flowimage are 1) the outer retina is expected to contain no vasculature in anormal eye, and 2) the vitreous is also expected to be vessel free.

In another embodiment of the present application, information about thesegmented layers from intensity OCT data may be used as an input tofurther aid the segmentation based on motion-contrast data or viceversa. In the preferred embodiment, RPE information from the intensityOCT data can be used to localize the region of interest for segmentationof the choriocapillaris based on motion-contrast data. Alternately,information about the location of the vascular layers from OCT motioncontrast data could be used to inform segmentations of the tissue layersthat are known to contain those vascular beds, including the ability tosegment the outer nuclear layer from inner retinal vasculature locationsand the ability to segment Bruch's membrane from the Choriocapillarislayer.

As previously mentioned, the motion contrast B-scan in FIG. 7 highlightsthe flow in the tissue. Line 702 is the segmentation from FIG. 6. Thesharpest edge of the motion contrast image can be identified by e.g.taking the gradient of the motion contrast image data. This line, shownas line 703 in FIG. 7, serves as a more accurate segmentation of thechoriocapillaris. As can be seen in FIG. 7, the segmentation line 702from the intensity data appears several pixels above the actualchoroidcapillaris layer in the figure. FIG. 8 is a zoomed in picture ofbox 701 in FIG. 7 corresponding to the region enclosed by the dashedbox, which further illustrates the inaccurate segmentation ofchoriocapillaris (703) that would be generated by directly using the RPEsegmentation (702) obtained from a traditional OCT image.

A main idea of this method is to use the flow contrast image asadditional information that is inherently registered to the structuralimage to improve identification of specific layers as well as to make itpossible to identify layers and boundaries that are not well delineatedin structural images. It may be necessary to correct the motion contrastimage for decorrelation tails to take full advantage of this method.Such correction might involve subtracting partial en face images basedon inner layers from that of outer layers that contain the decorrelationtails (see for example, Zhang, Anqi, Qinqin Zhang, and Ruikang K. Wang.“Minimizing projection artifacts for accurate presentation of choroidalneovascularization in OCT micro-angiography.” Biomedical Optics Express6.10 (2015): 4130-4143 hereby incorporated by reference).

Evaluating the Health of an eye Using Vessel Density

Microvasculature in the eye is affected by diabetic retinopathy (DR).With the development of OCT angiography, the microvasculature of the eyeis better visualized than in fluorescein angiography (FA), and someclinical experts have observed that the DR may be further advanced thanhad been appreciated in the normal clinical observations. In particular,the vessels appear less dense around the fovea for eyes with knowndiabetic retinopathy. Hence, a way to tell when an eye has early signsof microvascular damage is desirable. If this is possible with a regularOCT system, that is useful, but it would be even more useful if it couldbe done using a lower cost system.

The idea presented herein is to compare the density of themicrovasculature around the fovea to a database of normal eyes todetermine if there may be damage related to diabetes or other diseases.In particular, this could be done without a full cube scan of themacular region. A cluster of circle scans at a particular distance fromthe fovea may be sufficient to distinguish the status of the eye. Thiscould be done with a relatively slow (<27,000 A-scans/sec) OCT system.Because only a cluster of circle scans at a single radius around thefovea is acquired, it may be possible to dispense with tracking. A slowsystem with no tracking could be relatively inexpensive.

In one embodiment of the present application, an OCT angiography datasetis collected, said dataset including data that surrounds the fovea ofthe eye. The OCT angiography dataset comprises one or more cluster scanssuitable for motion contrast processing. In a preferred embodiment, thedataset comprises a cluster of circular scans centered on the foveahaving a particular radius. Alternatively, the dataset could be astandard OCT data cube, comprising clusters of B-scans. The volume couldbe centered on the fovea, or the fovea could later be identified in thevolume before processing as is well known by those skilled in the art(see for example U.S. Pat. No. 8,079,711 hereby incorporated byreference). The dataset is processed to generate vasculature informationusing any one of the previously described and referenced motion contrastalgorithm. A metric of the density of the vasculature at one or morelocations is determined. This metric is then compared to a database ofdensity values determined from a collection of normal eyes, and theresults of the comparison are stored or displayed to the user.

The density could be determined from motion contrast analysis of OCTdata (See for example U.S. Patent Publication No. 2013/0301008 thecontents of which are hereby incorporated by reference). The density canbe estimated by creating a skeletonized version of the vasculatureimage. This can be done by applying a Hessian filter or by other imageprocessing techniques that binarize the image. The binarized image maybe proportional to the size of the vessels in two dimensions, or it mayallow each vessel to be only one pixel wide. This prevents saturation ofthe calculation based on larger vessels that are not relevant to themicrovascular profile. Once the map is binarized, an ellipticalcalculation profile is estimated from the boundary of the fovealavascular zone, and the density is averaged around one or more ellipsesat given distances from the boundary of the FAZ. The foveal avascularzone in a motion contrast image can be automatically determined (see forexample U.S. Patent Publication No. 2013/0301008 hereby incorporated byreference).

FIGS. 9a and 9b show the microvascular density of the macula as afunction of distance from the boundary of the foveal avascular zone fora few different cases. FIG. 9a shows the data for the superficialretinal layer (SRL) and FIG. 9b shows the data for the deeper retinallayer (DRL). In general, the density starts low and increases away fromthe fovea. The upper and lower 5% limits of the density for a smallpopulation of normal eyes is shown (dotted for 5^(th) percentile anddashed for 95^(th) percentile). Overlaid on this normative data are thesame plots for an eye with non-proliferative diabetic retinopathy (NPDR,dotted dashed), and an eye with early diabetes, but no signs ofretinopathy (DM, long dashed), as well as an eye with a history ofcentral serous retinopathy (CSCR, solid bold) and one from a subjectwith multiple sclerosis (MS, solid unbold). The fact that the eye from asubject with a history of very early diabetes and no retinopathy (DMcase) is below normal for SRL and well below normal for DRL away fromthe fovea supports that a metric based on this information might beuseful for evaluating the health of the eye, such as screening forpathologies or wellness monitoring. Examining these plots, it is clearthat a cluster of circle scans that could identify and quantify thedensity of vasculature at ˜600 um from the foveal avascular zone couldbe a rapid way to measure the health of the eye, at least as it may beaffected by diabetes. Additional information may be gleaned by reviewingthe entire profile, or a map of the density, in both cases benefitingfrom a comparison to reference databases.

Although various applications and embodiments that incorporate theteachings of the present invention have been shown and described indetail herein, those skilled in the art can readily devise other variedembodiments that still incorporate these teachings.

What is claimed is:
 1. A method to evaluate the health of an eyesurrounding the fovea using optical coherence tomography (OCT)angiography, comprising: acquiring an OCT angiography dataset over aregion of the eye; processing the dataset to generate vasculatureinformation of the region of the eye; calculating a metric based uponthe density of vessels from the generated vasculature information;comparing the calculated metric with a collection of metrics calculatedfrom OCT angiography data acquired on eyes having normal vasculature,said comparing step to evaluate the health of the eye; and storing ordisplaying the results of the comparison or a further analysis thereof.2. A method as recited in claim 1, wherein the metric is calculatedbased on a skeletonized image of the vasculature.
 3. A method as recitedin claim 1, wherein the metric is calculated based on an average of thevessel density around an ellipse centered on the fovea.
 4. A method asrecited in claim 1, wherein the metric is calculated based on a functionof distance from the fovea or the foveal avascular zone.
 5. A method asrecited in claim 1, wherein the OCT angiography dataset comprises asingle cluster scan of circular B-scans centered on the fovea.
 6. Amethod as recited in claim 1, wherein the OCT angiography dataset is avolume that consists of clusters of B-scans centered on the fovea.
 7. Amethod for acquiring wide field optical coherence tomography (OCT)angiography images of the eye comprising: acquiring an OCT angiographydataset over a region of the eye, said region comprising multiplesub-regions; determining a quality metric for the dataset in each of thesub-regions; comparing the quality metrics to a predetermined thresholdto identify any sub-regions of sub-optimal data; acquiring replacementdata for any identified sub-regions; and generating an image from theacquired dataset, including the replacement data.
 8. A method as recitedin claim 7, wherein the OCT angiography data set is acquired for eachsub-region individually and there is a partial overlap between the datafor each sub-region.
 9. A method as recited in claim 8, furthercomprising stitching the data for each sub-region together to provide awide field of view image.
 10. A method as recited in claim 7, furthercomprising displaying the image to the user indicating the locations ofthe sub-optimal data and prompting the user to acquire the replacementdata.
 11. A method as recited in claim 7, wherein the replacement datais acquired automatically.
 12. A method as recited in claim 7, furthercomprising processing the OCT angiography dataset to generatevasculature information and wherein the quality metrics are determinedon the vasculature information.
 13. A method for identifying retinallayers in optical coherence tomography data of an eye of a patient, saidmethod comprising: collecting OCT data over a region of the eye, saiddata comprising multiple scans taken at approximately the same locationsin the region; generating structural information from the data;generating motion contrast information from the data by processing themultiple scans taken at approximately the same locations; identifyingone or more retinal layers in the eye using both the structural andmotion contrast information; and storing or displaying the resultinglayer identification or a further processing thereof.
 14. A method asrecited in claim 13, in which the layer identification involves creatinga cost function based on the structural and motion contrast information.15. A method as recited in claim 14, further comprising using agraph-based method to determine the location of the layer based on thecost function.
 16. A method as recited in claim 14, in which the layeridentification comprises identifying the layer in either the structuralor motion contrast information and then using the other information torefine the identification.
 17. A method as recited in claim 14, in whichthe structural B-scan is generated by averaging the scans taken atapproximately the same locations.
 18. A method as recited in claim 14,in which the OCT data is a volume of data comprising multiple B-scans.